The objective of this application is to determine how cognitive deficits in patients with age-related macular degeneration (AMD) are related to differences in functional connectivity and white matter integrity in the brain. AMD affects nearly 30% of Americans over age 75 and is associated with a two-fold increased risk of dementia. This team's previous NIH-funded work identified a strikingly prevalent cognitive deficit, even in non- demented AMD patients: poor verbal fluency. Yet there is a fundamental knowledge gap regarding the etiology of the link between AMD and cognitive deficits, and this gap impedes the development of strategies to reduce cognitive impairment in AMD. Our central hypothesis is that brain connectivity plays a critical role in understanding the link between AMD and cognitive deficits (e.g., verbal fluency). Specifically, the link may arise via two mechanisms: #1) vision changes from macular disease could have a negative effect on cognitive performance, or #2) a shared risk factor could promote damage in the brain and eye concurrently. These two mechanisms, which may both be at play, should be distinguished by different patterns of brain connectivity associated with AMD-related cognitive deficits. This will be the first study to combine longitudinal neurocognitive testing and brain imaging to better understand the extent and locus of brain changes in AMD. The study will include 120 people with AMD plus 120 age-, gender-, and education-matched adults without AMD. All participants will receive baseline and 2-year neurocognitive tests and a subset will provide structural and resting state functional magnetic resonance images (MRIs) at each time point. Aim 1 uses a neurocognitive battery developed and piloted by the applicants to define AMD-related differences in cognition without using tasks that require vision. Aims 2 & 3 use measures of functional brain connectivity and region- specific white matter integrity derived from functional MRI (fMRI) and diffusion tensor imaging (DTI). Aim 1. Characterize cognitive processes that contribute to verbal fluency deficit in AMD. We will construct regression models to test the extent to which verbal fluency performance in AMD reflects underlying cognitive deficits in semantic organization, processing speed, attention-switching, and memory. Aim 2. Identify differences in brain connectivity associated with verbal fluency, or its cognitive contributors. We will relate measures of cognitive ability to measures of intrinsic functional and structural connectivity in the brain. We will examie whether brain signatures associated with cognitive deficits differ among older adults with and without AMD. Aim 3. Identify cognitive profiles and brain signatures associated with cognitive decline in AMD patients. We will determine whether certain cognitive patterns or differences in specific neural networks, or both, predict AMD-related cognitive decline. Our working hypothesis is based on results of our pilot imaging study and favors mechanism #1: Verbal fluency deficit in AMD reflects problems in semantic organization and is related to differences in white matter tracts (primarily ventral) that support language, semantics, and vision.